Debunking the mysteries of deep reinforcement learning
Debunking the mysteries of deep reinforcement learning Demystifying one of the most interesting branches of AI States, rewards, and actions At the heart of everyreinforcement learningproblem are an agent and an environment. The environment provides information about the state of the system. The agent observes these states and interacts with the environment by taking actions. Actions can be discrete (e.g., flipping a switch) or continuous (e.g., turning a knob). These actions cause the environment to transition to a new state....